The main purpose of virtual reality (VR) is to enhance realism and the player experience. To do this, we focus on VR interaction design methods, analyze the existing interaction solutions including both accurate and rough interaction methods, and propose a new method for creating stable and realistic player interactions in a first-person shooter (FPS) game prototype. In this research, we design and modify the existing mapping methods between physical and virtual worlds, and create interfaces such that physical devices correspond to shooting tools in virtual reality. Moreover, we propose and design prototypes of universal interactions that can be implemented in a simple and straightforward way. Proposed interactions allow the player to perform actions similar to those of real shooting, using both hands such as firing, reloading, attaching and grabbing objects. In addition, we develop a gun template with haptic feedback, and a visual collision guide that can optionally be enabled. Then, we evaluate and compare our methods with the existing solutions. We then employ these in a VR FPS game prototype and conduct a user study with participants, and the resulting user study proves that the proposed method is more stable, player-friendly and realistic.

With the popularity of virtual reality (VR) games and devices, demand is increasing for estimating and displaying user motion in VR applications. Most pose estimation methods for VR avatars exploit inverse kinematics (IK) and online motion capture methods. In contrast to existing approaches, we aim for a stable process with less computation, usable in a small space. Therefore, our strategy has minimum latency for VR device users, from high-performance to low-performance, in multi-user applications over the network. In this study, we estimate the upper body pose of a VR user in real time using a deep learning method. We propose a novel method inspired by a classical regression model and trained with 3D motion capture data. Thus, our design uses a convolutional neural network (CNN)-based architecture from the joint information of motion capture data and modifies the network input and output to obtain input from a head and both hands. After feeding the model with properly normalized inputs, a head-mounted display (HMD), and two controllers, we render the user’s corresponding avatar in VR applications. We used our proposed pose estimation method to build single-user and multi-user applications, measure their performance, conduct a user study, and compare the results with previous methods for VR avatars.

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of data. In the latest decades, in the field of bioengineering data management, among others, eye-tracking data have become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which leads to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system was developed using the Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. The results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios, even if preliminary results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

The motion control of the virtual avatar enhances a sense of embodiment in a virtual reality (VR). Yet, the detailed relationship between motion control, assigned tasks, and the sense of embodiment remains unclear. We aim to investigate the relationships between degrees of control on a full-body avatar and three elements of the sense of embodiment: the sense of self-location, agency, and ownership in standalone and interaction tasks. To do this, we conducted a user study with three conditions of control over a full-body avatar. The types of control are (1) Low—control of an upper-body avatar, (2) Mid—control of a full-body avatar from three sensors, and (3) High—control of a full-body avatar from six sensors. These three control methods, which were used to animate the avatars and imitate the users’ pose, differ in accuracy and stability. Participants embodied three kinds of control and performed a single-user task (obstacle avoidance) and a multi-user task (catch-ball). Our results indicate that the degree of control impacts participants’ embodiment. However, there was no significant difference between high- and mid-control in the multi-user task, which was a different result from the single-user task. This suggests for virtual bodies that the participants capacity to control and see are the same or different, which may affect embodiment. Our result also shows that the multi-user task enhanced the sense of embodiment compared to the single-user task in the low- and mid-control avatars. Yet, the multi-user task decreased the sense of agency of the high-control avatar. This suggests that a failure of the assigned task may affect the sense of agency, especially when it is close to success, yielding revulsion. We further elucidate the insights into the relationship between the degree of control, the assigned tasks, and the elements of a sense of embodiment.